Bayesian modeling of time-varying and waning exposure effects

In epidemiologic studies, there is often interest in assessing the association between exposure history and disease incidence. For many diseases, incidence may depend not only on cumulative exposure, but also on the ages at which exposure occurred. This article proposes a flexible Bayesian approach for modeling age-varying and waning exposure effects. The Cox model is generalized to allow the hazard of disease to depend on an integral, across the exposed ages, of a piecewise polynomial function of age, multiplied by an exponential decay term. Linearity properties of the model facilitate posterior computation via a Gibbs sampler, which generalizes previous algorithms for Cox regression with time-dependent covariates. The approach is illustrated by an application to the study of protective effects of breastfeeding on incidence of childhood asthma.     

Bayesian modeling of age-specific survival in bird nesting studies under irregular visits

In this article, a Bayesian model for age-specific nest survival rates is presented to handle the irregular visit case. Both informative priors and noninformative priors are investigated. The reference prior under this model is derived, and, therefore, the hyperparameter specification problem is solved to some extent. The Bayesian method provides a more accurate estimate of the total survival rate than the standard Mayfield method, if the age-specific hazard rates are not constant. The Bayesian method also lets the biologist look for high- and low-survival rates during the whole nesting period. In practice, it is common for data of several types to be collected in a single study. That is, some nests may be aged, others are not. Some nests are visited regularly; others are visited irregularly. The Bayesian method accommodates any mix of these sampling techniques by assuming that the aging and visiting activities have no effect on the survival rate. The methods are illustrated by an analysis of the Missouri northern bobwhite data set.     

Semiparametric approaches for joint modeling of longitudinal and survival data with time-varying coefficients

Summary .   We study joint modeling of survival and longitudinal data. There are two regression models of interest. The primary model is for survival outcomes, which are assumed to follow a time-varying coefficient proportional hazards model. The second model is for longitudinal data, which are assumed to follow a random effects model. Based on the trajectory of a subject's longitudinal data, some covariates in the survival model are functions of the unobserved random effects. Estimated random effects are generally different from the unobserved random effects and hence this leads to covariate measurement error. To deal with covariate measurement error, we propose a local corrected score estimator and a local conditional score estimator. Both approaches are semiparametric methods in the sense that there is no distributional assumption needed for the underlying true covariates. The estimators are shown to be consistent and asymptotically normal. However, simulation studies indicate that the conditional score estimator outperforms the corrected score estimator for finite samples, especially in the case of relatively large measurement error. The approaches are demonstrated by an application to data from an HIV clinical trial.     

Understanding the separation of timescales in bacterial proteasome core particle assembly

The 20S proteasome core particle (CP) is a molecular machine that is a key component of cellular protein degradation pathways. Like other molecular machines, it is not synthesized in an active form but rather as a set of subunits that assemble into a functional complex. The CP is conserved across all domains of life and is composed of 28 subunits, 14 α and 14 β, arranged in four stacked seven-member rings (α₇β₇β₇α₇). While details of CP assembly vary across species, the final step in the assembly process is universally conserved: two half proteasomes (HPs; α₇β₇) dimerize to form the CP. In the bacterium Rhodococcus erythropolis, experiments have shown that the formation of the HP is completed within minutes, while the dimerization process takes hours. The N-terminal propeptide of the β subunit, which is autocatalytically cleaved off after CP formation, plays a key role in regulating this separation of timescales. However, the detailed molecular mechanism of how the propeptide achieves this regulation is unclear. In this work, we used molecular dynamics simulations to characterize HP conformations and found that the HP exists in two states: one where the propeptide interacts with key residues in the HP dimerization interface and likely blocks dimerization, and one where this interface is free. Furthermore, we found that a propeptide mutant that dimerizes extremely slowly is essentially always in the nondimerizable state, while the wild-type rapidly transitions between the two. Based on these simulations, we designed a propeptide mutant that favored the dimerizable state in molecular dynamics simulations. In vitro assembly experiments confirmed that this mutant dimerizes significantly faster than wild-type. Our work thus provides unprecedented insight into how this critical step in CP assembly is regulated, with implications both for efforts to inhibit proteasome assembly and for the evolution of hierarchical assembly pathways.     

Glucan effect on the survival of mice after radiation exposure

The effect of glucan (beta-1,3-polyglucose) was investigated on the radiation-induced damage to the system of non-specific immunity in mice. A positive influence of glucan administered before exposure to a dose of 200 R was observed on the following parameters of postradiation regeneration: while blood cell count, due mainly to increased granulocyte count, increased per cent of peroxidase-positive cells in bone marrow, increased mass and cellularity of the spleen, in relation to the group of the animals not receiving the preparation. Administration of glucan 5 days before exposure to 650 R of X-rays prolonged the mean survival time. Administration of the preparation after exposure to radiation had no significant effect on the survival time of mice.     

The effect of a myocardial infarction on the normalized time-varying elastance curve.

It has been suggested that the shape of the normalized time-varying elastance curve [E(n)(t(n))] is conserved in different cardiac pathologies. We hypothesize, however, that the E(n)(t(n)) differs quantitatively after myocardial infarction (MI). Sprague-Dawley rats (n = 9) were anesthetized, and the left anterior descending coronary artery was ligated to provoke the MI. A sham-operated control group (CTRL) (n = 10) was treated without the MI. Two months later, a conductance catheter was inserted into the left ventricle (LV). The LV pressure and volume were measured and the E(n)(t(n)) derived. Slopes of E(n)(t(n)) during the preejection period (alpha(PEP)), ejection period (alpha(EP)), and their ratio (beta = alpha(EP)/alpha(PEP)) were calculated, together with the characteristic decay time during isovolumic relaxation (tau) and the normalized elastance at end diastole (E(min)(n)). MI provoked significant LV chamber dilatation, thus a loss in cardiac output (-33%), ejection fraction (-40%), and stroke volume (-30%) (P < 0.05). Also, it caused significant calcium increase (17-fold), fibrosis (2-fold), and LV hypertrophy. End-systolic elastance dropped from 0.66 +/- 0.31 mmHg/microl (CTRL) to 0.34 +/- 0.11 mmHg/microl (MI) (P < 0.05). Normalized elastance was significantly reduced in the MI group during the preejection, ejection, and diastolic periods (P < 0.05). The slope of E(n)(t(n)) during the alpha(PEP) and beta were significantly altered after MI (P < 0.05). Furthermore, tau and end-diastolic E(min)(n) were both significantly augmented in the MI group. We conclude that the E(n)(t(n)) differs quantitatively in all phases of the heart cycle, between normal and hearts post-MI. This should be considered when utilizing the single-beat concept.     

Nonlinear and time-dependent effects of sparsely measured continuous time-varying covariates in time-to-event analysis

Many flexible extensions of the Cox proportional hazards model incorporate time-dependent (TD) and/or nonlinear (NL) effects of time-invariant covariates. In contrast, little attention has been given to the assessment of such effects for continuous time-varying covariates (TVCs). We propose a flexible regression B-spline–based model for TD and NL effects of a TVC. To account for sparse TVC measurements, we added to this model the effect of time elapsed since last observation (TEL), which acts as an effect modifier. TD, NL, and TEL effects are estimated with the iterative alternative conditional estimation algorithm. Furthermore, a simulation extrapolation (SIMEX)-like procedure was adapted to correct the estimated effects for random measurement errors in the observed TVC values. In simulations, TD and NL estimates were unbiased if the TVC was measured with a high frequency. With sparse measurements, the strength of the effects was underestimated but the TEL estimate helped reduce the bias, whereas SIMEX helped further to correct for bias toward the null due to “white noise” measurement errors. We reassessed the effects of systolic blood pressure (SBP) and total cholesterol, measured at two-year intervals, on cardiovascular risks in women participating in the Framingham Heart Study. Accounting for TD effects of SBP, cholesterol and age, the NL effect of cholesterol, and the TEL effect of SBP improved substantially the model's fit to data. Flexible estimates yielded clinically important insights regarding the role of these risk factors. These results illustrate the advantages of flexible modeling of TVC effects.     

From exposure to effect: a comparison of modeling approaches to chemical carcinogenesis.

Standardized long-term carcinogenicity tests aim to reveal the relationship between exposure to a chemical and occurrence of a carcinogenic response. The analysis of such tests may be facilitated by the use of mathematical models. To what extent current models actually achieve this purpose is difficult to evaluate. Various aspects of chemically induced carcinogenesis are treated by different modeling approaches, which proceed very much in isolation of each other. With this paper we aim to provide for the non-mathematician a comprehensive and critical overview of models dealing with processes involved in chemical carcinogenesis. We cover the entire process of carcinogenesis, from exposure to effect. We succinctly summarize the biology underlying the models and emphasize the relationship between model assumptions and model formulations. The use of mathematics is restricted as far as possible with some additional information relegated to boxes.     

Identifying local drivers of a vector-pathogen-disease system using Bayesian modeling

‘Bois noir’ is a phytoplasma-mediated grapevine yellows disease that causes great economic damage in European vineyards. Previous studies have examined habitat relationships on a regional scale, which help to better understand the large-scale epidemiology. Local drivers, such as micro-habitat preferences of the vector (Hyalesthes obsoletus, a cixiid planthopper), or local interactions with reservoir host plants, however, are still poorly understood, although this knowledge is crucial for developing site-specific management strategies.Here, we examined the local environment-species relationships of a phytoplasma-mediated grapevine disease on a scale of 15 m in a 2.9 ha vineyard using: (i) data on elevation and habitat types; (ii) cover of host plants Urtica dioica and Convolvulus arvensis over three seasons, (iii) vector monitoring over four seasons; (iv) genetic tests for phytoplasma presence in the vector; and (v) inspection of 6056 grapevine plants for visual symptoms of the ‘bois noir’ disease. The data were analyzed in a joint causal model that describes the interplay between vector, pathogen, disease and environment, estimated with Bayesian inference.Our results indicate that surrounding natural and semi-natural vegetation (fallow land, forest and managed agricultural land) and high density of the major host plant U. dioica are associated with an increase in vector population densities. Higher vector population densities at low availability of U. dioica were associated with higher phytoplasma infection rates in the vector. The prevalence of disease symptoms in grapevine plants was nonetheless more affected by grapevine cultivar and higher elevation than by the estimated availability of infected vectors.The results of our local analysis support current bois noir management recommendations stating that (1) removal of the host plant U. dioica should be best carried out in either spring or autumn; and (2) grapevine cultivars are unequally susceptible. Moreover, we provide evidence that U. dioica control before the flight period may result in low U. dioica densities and high H. obsoletus population densities, causing an increase in vector infection rates and disease pressure.     

Bayesian modeling of recombination events in bacterial populations

Background  

We consider the discovery of recombinant segments jointly with their origins within multilocus DNA sequences from bacteria representing heterogeneous populations of fairly closely related species. The currently available methods for recombination detection capable of probabilistic characterization of uncertainty have a limited applicability in practice as the number of strains in a data set increases.     

Hierarchical Bayesian modeling in dichotomous processes in the presence of nonresponse

Sampling units that do not answer a survey may dramatically affect the estimation results of interest. The response may even be conditional on the outcome of interest in the survey. If estimates are found using only those who responded, the estimate may be biased, known as nonresponse bias. We are interested in finding estimates of success rates from a survey. We begin by looking at two current Bayesian approaches to treating nonresponse in a hierarchical model. However, these approaches do not consider possible spatial correlations between domains for either success rate or response rate. We build a Bayesian hierarchical spatial model to explicitly estimate the success rate, response rate given success, and response rate given failure. The success rates in the domains of the survey are allowed to be spatially correlated. We also allow spatial dependence between domains in both response rate given success and response rate given failure. Spatial dependence is induced by a common latent spatial structure between the two conditional response rates. We use the 1998 Missouri Turkey Hunting Survey to illustrate this methodology. We find significant spatial correlation in the success rates and incorporating nonrespondents has an impact on the success rate estimates.     

The mere exposure effect in the domain of haptics

Background

Zajonc showed that the attitude towards stimuli that one had been previously exposed to is more positive than towards novel stimuli. This mere exposure effect (MEE) has been tested extensively using various visual stimuli. Research on the MEE is sparse, however, for other sensory modalities.

Methodology/Principal Findings

We used objects of two material categories (stone and wood) and two complexity levels (simple and complex) to test the influence of exposure frequency (F0 = novel stimuli, F2 = stimuli exposed twice, F10 = stimuli exposed ten times) under two sensory modalities (haptics only and haptics & vision). Effects of exposure frequency were found for high complex stimuli with significantly increasing liking from F0 to F2 and F10, but only for the stone category. Analysis of “Need for Touch” data showed the MEE in participants with high need for touch, which suggests different sensitivity or saturation levels of MEE.

Conclusions/Significance

This different sensitivity or saturation levels might also reflect the effects of expertise on the haptic evaluation of objects. It seems that haptic and cross-modal MEEs are influenced by factors similar to those in the visual domain indicating a common cognitive basis.     

The effect of low-molecular-weight heparin in the survival of a rabbit congested skin flap

Swelling and congestion of flaps are frequently seen postoperatively and can cause unexpected necrosis. According to previous reports, venous thrombosis seems to be a more frequent problem than arterial occlusion in both experimental and clinical surgery. Few satisfactory venous trauma models exist, and reports on experimental venous thrombosis are rare. The object of this study was to create a rabbit venous occlusion flap model and to evaluate the effect of low-molecular-weight heparin on this flap. Eight New Zealand rabbits were used in the pilot study, in which the ideal congested flap was investigated using a flap pedicle based on the central auricular artery with a skin pedicle 0, 1, 2, or 3 cm wide. The flap (3 x 6 cm) was designed on the central part of the left ear, and the central auricular vein and nerve, the former for venous return, were cut out at the base of the flap. The flaps with skin pedicles 0, 1, 2, or 3 cm wide showed mean necrosis length of 60.0, 9.3, 4.2, and 0.0 mm, respectively. The flaps with skin pedicles 0, 1, 2, or 3 cm wide showed mean necrosis of 100, 15.5, 7, and 0 percent, respectively. Therefore, the flap, based on a 1-cm-wide skin pedicle and the central auricular artery, was selected as an optimal congested flap model showing 15.5 percent necrosis. The congested flap was then elevated on the left ear of another 10 rabbits. Subcutaneous low-molecular-weight heparin (320 IU/kg) was administered immediately after surgery to five of the rabbits (the low-molecular-weight heparin group), and the remaining five were used as a control group. Fluorescein was injected 15 minutes after surgery to evaluate the circulatory territory of the flap, and the circulatory territory was measured 5 minutes after injection. The flaps were assessed 7 days after surgery by angiography, histology, and clinical findings. The circulatory territory was significantly greater in the low-molecular-weight heparin group (mean +/- SD, 39.2 +/- 3.0 mm) than the control group (mean +/- SD, 48.0 +/- 1.0 mm) (p < 0.001) assessed 7 days after surgery. The longest flap survival length in group A and group B ranged from 40 to 55 mm (mean +/- SD. 49.4 +/- 5.6 mm) and complete survival (mean +/- SD, 60.0 +/- 0.0 mm). The improvement in survival was statistically significant for group B compared with group A (p < 0.015). Histologic evaluation revealed moderate to severe venous congestion and inflammation in the control group, whereas there were minimal changes in the low-molecular-weight heparin group. Angiography of the flap revealed obvious venous occlusion in the periphery in the control group compared with the low-molecular-weight heparin group. The authors conclude that subcutaneous administration of low-molecular-weight heparin has a great potential to improve the survival length of a congested flap without major complications.     

The effect of temperature on the survival of microorganisms in a deep space vacuum

A Space Molecular Sink Research Facility (Molsink) was used to evaluate the ability of microorganisms to survive the vacuum of outer space. this facility could be programmed to simulate flight spacecraft vacuum environments at pressures in the 10^–10 torr range and thermal gradients (30 to 60°C) closely associated to surface temperatures of inflight spacecraft.Initial populations ofStaphylococcus epidermidis and aMicrococcus sp. were reduced approximately 1 log while exposed to –105 and 34°C, and approximately 2 logs while exposed to 59°C for 14 days in the vacuum environment.Spores ofBacillus subtilis var.niger were less affected by the environment. Initial spore populations were reduced 0.2, 0.3, and 0.8 log during the 14-day vacuum exposure at –124, 34, and 59°C, respectively.This paper presents the results of one phase of research carried out at the Jet Propulsion Laboratory, California Institute of Technology, under Contract No. NAS 7-100, sponsored by the National Aeronautics and Space Administration.     

Bayesian state-space modeling of metapopulation dynamics in the Glanville fritillary butterfly

The complexity of mathematical models of ecological dynamics varies greatly, and it is often difficult to judge what would be the optimal level of complexity in a particular case. Here we compare the parameter estimates, model fits, and predictive abilities of two models of metapopulation dynamics: a detailed individual-based model (IBM) and a population-based stochastic patch occupancy model (SPOM) derived from the IBM. The two models were fitted to a 17-year time series of data for the Glanville fritillary butterfly (Melitaea cinxia) inhabiting a network of 72 small meadows. The data consisted of biannual counts of larval groups (IBM) and the annual presence or absence of local populations (SPOM). The models were fitted using a Bayesian state-space approach with a hierarchical random effect structure to account for observational, demographic, and environmental stochasticities. The detection probability of larval groups (IBM) and the probability of false zeros of local populations (SPOM) in the observation models were simultaneously estimated from the time-series data and independent control data. Prior distributions for dispersal parameters were obtained from a separate analysis of mark–recapture data. Both models fitted the data about equally, but the results were more precise for the IBM than for the SPOM. The two models yielded similar estimates for a random effect parameter describing habitat quality in each patch, which were correlated with independent empirical measures of habitat quality. The modeling results showed that variation in habitat quality influenced patch occupancy more through the effects on movement behavior at patch edges than on carrying capacity, whereas the latter influenced the mean population size in occupied patches. The IBM and the SPOM explained 63% and 45%, respectively, of the observed variation in the fraction of occupied habitat area among 75 independent patch networks not used in parameter estimation. We conclude that, while carefully constructed, detailed models can have better predictive ability than simple models, this advantage comes with the cost of greatly increased data requirements and computational challenges. Our results illustrate how complex models can be helpful in facilitating the construction of effective simpler models.